AI Engineer

AIFund•Mountain View, CA

10d

About The Position

AI Fund is a venture studio founded by Dr. Andrew Ng in 2017. Our portfolio companies use AI technology to build applications across numerous industry sectors. The AI Fund team combines their experiences as AI pioneers, entrepreneurs, venture capitalists, investors, and operators. We are backed by a $390-million dollar fund from top-tier global corporations and VC firms. Our purpose is to build AI companies that move humanity forward. We are seeking an AI engineer with architecture judgment, product instincts, and fluency across the modern AI application stack. You should be able to decompose AI products into composable building blocks: model selection, prompting, tool use, retrieval, structured outputs, memory and state, workflow orchestration, planning, reflection, evaluation, observability, and safety controls. You should understand the reason each component belongs in a system, the tradeoffs it introduces, and the evidence needed to know whether it is working. You start with the simplest design that can answer the open question about an idea, and tighten the architecture only as evidence justifies it. You follow where AI is heading and ground engineering decisions in product goals, user behavior, the data available for the problem, system constraints, and measured performance. You will collaborate closely with product thinkers, designers, AI experts, and engineers to validate or falsify venture ideas through functional prototypes.

Requirements

3+ years of software engineering experience, including end-to-end ownership of at least one production AI application architecture spanning UI, backend, data, models, tools, and evaluation.
Demonstrated experience building applications that use large language models, multimodal models, or other modern AI capabilities in product workflows.
Strong technical fluency across frontend, backend, APIs, databases, and cloud deployment, with enough depth to review, debug, and steer implementation.
Expert ability to work with frontier coding agents, including writing precise specs, decomposing work, inspecting generated code, catching architectural mistakes, and deciding when to intervene directly.
Ability to justify retrieval choices against corpus structure, freshness, permissions, latency, precision, recall, and cost.
Experience with SQL and NoSQL data systems, including the ability to model data for application use, retrieval, analytics, and operational reliability.
Strong communication skills and the ability to work collaboratively across disciplines.
Habit of reading papers, model cards, technical postmortems, and production writeups, then folding useful lessons into the next build.

Nice To Haves

Experience shipping MVPs, prototypes, or early-stage products under ambiguity.
Experience as a technical lead, architect, founding engineer, or senior builder on AI-driven products.
Contributions to open-source AI, developer tools, evals, retrieval, agents, or applied ML infrastructure.
Interest or experience in product design, product strategy, or company creation.

Responsibilities

Build full-stack AI prototypes that pressure-test venture ideas before founder or entrepreneur-in-residence handoff.
Design AI systems from composable building blocks and make the tradeoffs visible to product and engineering partners.
Choose retrieval and context strategies that fit the data and task, from structured queries and hybrid search to reranking, graph traversal, and long-context or human-curated context.
Build agentic and workflow-based systems with clear control flow, bounded autonomy, useful tool interfaces, state management, recovery paths, and human review where appropriate.
Make architecture and platform choices that fit the stage of an idea, keeping prototypes cheap to change while leaving a credible path to production if the idea validates.
Build and integrate APIs, databases, third-party services, internal tools, and cloud infrastructure.
Define evaluation loops for AI behavior, including task success, retrieval quality, factuality, tool-call correctness, grounding, safety, latency, cost, and user-perceived quality.
Use error analysis to decide whether to improve prompts, data, retrieval, tools, orchestration, model choice, UX, or product scope.
Collaborate cross-functionally with product, design, and AI experts to create, test, and iterate on new concepts using direct user feedback.
Present build results to potential entrepreneurs-in-residence and founders: what worked, what failed, what they need to know to decide next steps.
Direct frontier coding agents to turn clear product and technical intent into working software, while owning the architecture, review, debugging, and quality bar.
Identify and troubleshoot issues across the full stack, including frontend, backend, AI orchestration, data pipelines, deployment, and production behavior.
Contribute to better development processes, reusable engineering practices, and shared technical judgment across the team.